Fraud detection is a classic adversarial analytics challenge: As soon as an automated system successfully learns to stop one scheme, fraudsters move on to attack another way. Each scheme requires looking for different signals (i.e. features) to catch, is relatively rare (one in millions for finance or ecommerce, for example), and it may take months to investigate a single case (in healthcare or tax, for example) – making quality training data scarce. This talk will cover, via live demo & code walk-through, the key lessons we’ve learned while building such real-world software systems over the past few years. We’ll incrementally build a hybrid machine learned model for fraud detection, combining features from natural language processing, topic modeling, time series analysis, link analysis, heuristic rules & anomaly detection. We’ll be looking for fraud signals in public email datasets, using Python & popular open-source libraries for data science and Apache Spark as the compute engine for scalable parallel processing.

1. 11
Online fraud detection:
A reference architecture for adversarial learning
David Talby
@davidtalby
SVP Engineering, Atigeo

2. 2
Why Semi-Supervised Learning & Feedback?
50+Schemes
(and counting)
99.9999%‘Good’ messages
6+Months
per case

3. 3
Why Hybrid Analytics?
Ignore
more rules
Unusual
timing of
eventsUnusual
personal
network
Teamwork
& scale
Think & talk
differently

4. 4
Why Hybrid Analytics?
Rule
Inference
Time
Series
Analysis
Link
Analysis
Ensemble
Learning
Natural
Language

5. 5
Stream processing
Kafka
Email Stream
Account transactions
Stream
Email NLP
Features
People graph
Transactions time series

6. 6
User Analysis Iteration
Email NLP
Features
User graph
Transactions
time series
Graph Features
Time Series
Features
NLP Features
Agent Feedback
Train/TestClassifier

7. 77
Thank you!
Source code available on request:
David.Talby@atigeo.com
@davidtalby

8. © 2015 Atigeo, Corporation. All rights reserved. Atigeo and the xPatterns logo are trademarks of Atigeo. The information herein is for informational purposes only and represents the current view of Atigeo as of the date
of this presentation. Because Atigeo must respond to changing market conditions, it should not be interpreted to be a commitment on the part of Atigeo, and Atigeo cannot guarantee the accuracy of any information
provided after the date of this presentation. ATIGEO MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

9. 9

10. 10

11. 11

12. 12

13. 13

14. 14

15. 15

16. 16

17. 17